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Linear_Matrix_Inequalities_in_System - 资源详细说明
The basic topic of this book is solving problems from system and control theory using
convex optimization. We show that a wide variety of problems arising in system
and control theory can be reduced to a handful of standard convex and quasiconvex
optimization problems that involve matrix inequalities. For a few special cases there
are “analytic solutions” to these problems, but our main point is that they can be
solved numerically in all cases. These standard problems can be solved in polynomial-
time (by, e.g., the ellipsoid algorithm of Shor, Nemirovskii, and Yudin), and so are
tractable, at least in a theoretical sense. Recently developed interior-point methods
for these standard problems have been found to be extremely efficient in practice.
Therefore, we consider the original problems from system and control theory as solved.
convex optimization. We show that a wide variety of problems arising in system
and control theory can be reduced to a handful of standard convex and quasiconvex
optimization problems that involve matrix inequalities. For a few special cases there
are “analytic solutions” to these problems, but our main point is that they can be
solved numerically in all cases. These standard problems can be solved in polynomial-
time (by, e.g., the ellipsoid algorithm of Shor, Nemirovskii, and Yudin), and so are
tractable, at least in a theoretical sense. Recently developed interior-point methods
for these standard problems have been found to be extremely efficient in practice.
Therefore, we consider the original problems from system and control theory as solved.
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